Flood risk management has traditionally been centered around economic damages and casualty assessments, providing a basis for preventive measures like levees. However, with increasing climate change-induced weather extremes, a shift toward flood resilience is necessary. This shift involves accepting some level of flooding while enhancing system recovery and damage mitigation. Existing resilience frameworks offer only qualitative insights or rely on significant assumptions, limiting their applicability in objective assessments.

This research focuses on addressing the challenges of quantifying flood resilience, a concept inherently complex due to its multi-layered nature and reliance on diverse perspectives. By incorporating hydrodynamic conditions such as water depth, flow velocity, and momentum, alongside local topography and land use, this study aims to propose metrics that better capture the temporal and spatial dynamics of flood resilience. This enables objective evaluation of mitigation measures, guides resource allocation, and facilitates informed decision-making for engineers and policymakers alike. This could subsequently enhance flood risk frameworks and increase flood safety in The Netherlands.

A methodology for quantifying flood resilience through hydrodynamic modeling is presented in this study. It identifies functionality variables like temporal water depth, temporal impact, number of flooded buildings, and percentage dry area as central to assessing resilience. From these functionalities, resilience metrics are derived, like shock amplitude, arrival time, and residence time.

The methodology is tested through a case study in the Alblasserwaard. The study uses 3Di modeling software to simulate flooding scenarios and evaluate the applicability of resilience measures. This is done through model variations and interventions such as detention basins, moveable barriers, and enhanced pumping capacity. The chosen case study includes diverse land uses, enabling the assessment of resilience across residential, economic, and ecological perspectives.

The metrics of shock rate, residence time, flood arrival time, and flooded utilities provide promising insights into flood resilience. The derivative shock rate evaluates emergency response service capacity, while residence time assesses damage extent and recovery time. Adding indirect hydrodynamic conditions, like nearby flooded roads and utilities, further enhances system understanding in the provided case study. Different perspectives highlight the variability in suitable metrics as well as suitable interventions.

However, some metrics require further research or modification. The depth integrals show potential during shock and recovery, but they lose information when used as a linear metric between time and water depth. The flooded utilities metric provides valuable insights but needs expansion to accurately reflect flooding consequences. The momentum impact metrics are unsuitable for the current model due to limitations in 3Di’s flow velocity calculations.

While the method proved feasible for identifying and comparing
resilience in a specific system, further research is needed to address uncertainties, refine metrics for indirect effects, and test applicability beyond the presented case study. This framework represents a further development toward a comprehensive, objective approach to flood resilience, supporting effective, adaptable flood
management solutions even under the continuous threat of increased climate extremes.

This report details the development of a Flood Early Warning System (FEWS) for the Tana Basin in Kenya, executed by a multidisciplinary team from the Delft University of Technology. Recognizing the Tana Basin’s vulnerability to flood risks, exacerbated by climatic variability, limited funds, and limited available data, the project proposes a model that combines computationally efficient hydrological and hydrodynamic modelling with robust stakeholder collaboration. The study area comprises the entire Tana Basin, with a specific focus on the flood-prone area near Garissa used for validation. The FEWS developed incorporates local and scientifi-cally derived knowledge to forecast floods, aiming to aid the transition from a technologically intermediate to a technologically advanced FEWS. Through an iterative process of model selection, validation, and stakeholder feedback, the system attempts to integrate the GR4J hydrological model in SuperflexPy and combines this with the Super Fast INundation of CoastS (SFINCS) model. Data sources include global remote sensing datasets like FABDEM & CHIRPS. Furthermore, it uses the water level gauge data provided by the Water Resource Authority of Kenya, as well as TAHMO weather station data.

The report concludes by reflecting on the modelling techniques for both the hydrological and hydrodynamic models and provides recommendations for the further development of a FEWS in the Tana Basin in Kenya. The implementation of the hydrological model was not able to propagate external flows through the network, making it poorly suited for use in the Tana Basin. The hydrodynamic model works decently well in flood conditions but overpredicts flooding during regular flow conditions. Recommendations on stakeholder engagements and data-sharing practices to foster a resilient flood management system in the Tana Basin include more comprehensive Memoranda of Understanding (MoU) and stricter adherence to the Disaster Risk Management Framework of the United Nations.